Advances in machine learning have enabled computing devices to solve complex problems in many fields. For example, image analysis (e.g., face recognition), natural language processing, and many other fields have benefitted from the use of machine learning techniques. For certain types of problems, advanced computing techniques, such as genetic algorithms or backpropagation, may be available to develop a neural network. In one example, a genetic algorithm may apply neuroevolutionary techniques over multiple epochs to evolve candidate neural networks to model a training data set. Neural networks generally do not describe a human-understandable relationship between input data and output data. Stated another way, it is generally not clear, from a human perspective, whether or why a specific neural network would be expected to produce a reliable result. Accordingly, there may not be a quick way to determine whether a particular candidate neural network that is produced by the genetic algorithm is likely or unlikely to be accurate or reliable. Rather, during each epoch, evaluating the fitness of candidate neural networks may be time consuming and may involve passing the entirety of a testing data set through each of the candidate neural networks.